Native Painted Turtles (cpb) 2008-2017

Statistical Analysis of a turtle population "Chysemmys picta belli" at two locations in the pacific north west surveyed between 2008 and 2017

Data cleaning

turtles.py and helpers.py consist of custom helper functions and standard panda acrobatics to read data from excel and do some basic data transformation.

In [1]:
#Prepping the notebook
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# my custom helper functions
import helpers as hlp
import turtles
import scipy
import pandas as pd
#filter data: Naitive turle and relevant
print ("Filtering Natives ...")
natives = turtles.get_clean_data()
natives = natives[natives['Weight']!=0]
natives = natives[natives['Carapace']!=0]
natives = natives[natives['Plastron']!=0]
natives = natives[natives['Species']=='Cpb']
print ("Done")

Filtering Natives ...
Loading data /Users/kim/Projects/turtles-way-down/source/Turtle Data.xls
2008
2009
2010
2011
2012
2013
2014
Cleaning decimals ...
Cleaning other values ...
Loading data /Users/kim/Projects/turtles-way-down/source/MF Trapping Data.xlsx
Cleaning decimals ...
Cleaning other values ...
Done

EDA - Exploratory Data Analysis

basic metrics

In [2]:
#Show some data - basic metrics
natives[['Carapace','Plastron','Annuli','Weight']].describe()
Out[2]:
Carapace Plastron Annuli Weight
count 888.000000 888.000000 888.000000 888.000000
mean 140.332336 132.643539 5.436937 470.087524
std 39.513756 38.517132 3.438044 362.312073
min 32.740002 29.730000 0.000000 7.050000
25% 110.362501 103.987499 3.000000 197.750000
50% 132.339996 125.000000 5.000000 318.000000
75% 175.520000 165.000000 7.000000 694.750000
max 237.199997 242.199997 20.000000 1435.000000
In [3]:
#Show some data - Gender, Location etc
natives[['Species','Capture Location','Source','ID']].pivot_table(index=['Capture Location','Source','Species'],aggfunc='count')
Out[3]:
ID
Capture Location Source Species
Gresham Turtle Data.xls|2008 Cpb 48
Turtle Data.xls|2009 Cpb 14
Turtle Data.xls|2010 Cpb 48
Turtle Data.xls|2011 Cpb 114
Turtle Data.xls|2012 Cpb 187
Turtle Data.xls|2013 Cpb 103
Turtle Data.xls|2014 Cpb 225
Mason Flats MF Trapping Data.xlsx|All Capture Data Cpb 149

Swarmplots

Swarmplots to show distribution of Carapace, Plastron and Weight by Gender

In [4]:
### Basic Swarmplot to show Carapace / Plastron / Weight by Gender
plt.style.use('seaborn-notebook')
plt.rcParams['figure.dpi'] = 300
colors = [sns.xkcd_rgb['pale red'],sns.xkcd_rgb['denim blue'],sns.xkcd_rgb['medium green']]

print ("Plotting swarm plot with " + str(natives.Weight.count()) + ' samples...')
sns.set()
_ = plt.figure(1)
_ = plt.suptitle('Native Turtles Swarmplots ')

_1 =plt.subplot(2,2,1)
_ = sns.swarmplot(x='Gender', y='Carapace', data=natives,size=3,palette=colors)
_ = plt.ylabel('Carapace')

_2 =plt.subplot(2,2,2,sharey=_1)
_ = sns.swarmplot(x='Gender', y='Plastron', data=natives,size=3,palette=colors)
_ = plt.ylabel('Plastron')
_ = plt.tight_layout(rect=[0, 0.03, 1, 0.95])
_ = plt.subplot(2,2,3)
_ = sns.swarmplot(x='Gender', y='Weight', data=natives,size=3,palette=colors)
_ = plt.xlabel('Gender')
_ = plt.ylabel('Weight')

Plotting swarm plot with 888 samples...
In [5]:
natives_melted = natives.copy()
natives_melted['Weight / 10'] = natives_melted.Weight.div(10)
natives_melted = pd.melt(natives_melted[['Carapace','Plastron','Weight / 10','Gender']],'Gender',var_name='measurement')

_ = sns.swarmplot(x="measurement", y="value", hue="Gender", data=natives_melted,palette=colors)
_ = plt.show()

ECDFs

using Empirical Cumulative Distribution Function do display exactly the same information as the swarmplots above.

In [6]:
### Same information as beswars above just displayed as ECDFs
plt.style.use('seaborn-notebook')
plt.rcParams['figure.dpi'] = 300
colors = [sns.xkcd_rgb['pale red'],sns.xkcd_rgb['denim blue']]


print ("Plotting Cumulative Distribution Function with " + str(natives.Weight.count()) + ' samples...')
# Compute ECDFs

_ = plt.figure(2)
_ = plt.suptitle('Native Turtles Cumulative Distribution ')

x_CarapaceF, y_CarapaceF = hlp.ecdf(natives[natives['Gender']=='f']['Carapace'])
x_CarapaceM, y_CarapaceM = hlp.ecdf(natives[natives['Gender']=='m']['Carapace'])

_ = plt.subplot(2,2,1)
_ = plt.plot(x_CarapaceF, y_CarapaceF, marker='.',linestyle = 'none',color=colors[0])
_ = plt.plot(x_CarapaceM, y_CarapaceM, marker='.',linestyle = 'none',color=colors[1])
_ = plt.margins(0.02)
_ = plt.legend(('Female', 'Male'), loc='lower right')
_ = plt.xlabel('Carapace (mm)')
_ = plt.ylabel('ECDF')

x_PlastronF, y_PlastronF = hlp.ecdf(natives[natives['Gender']=='f']['Plastron'])
x_PlastronM, y_PlastronM = hlp.ecdf(natives[natives['Gender']=='m']['Plastron'])

_ = plt.subplot(2,2,2)
_ = plt.plot(x_PlastronF, y_PlastronF, marker='.',linestyle = 'none',color=colors[0])
_ = plt.plot(x_PlastronM, y_PlastronM, marker='.',linestyle = 'none',color=colors[1])
_ = plt.margins(0.02)
_ = plt.legend(('Female', 'Male'), loc='lower right')
_ = plt.xlabel('Plastron (mm)')
_ = plt.ylabel('ECDF')
_ = plt.tight_layout(rect=[0, 0.03, 1, 0.95])

x_WeightF, y_WeightF = hlp.ecdf(natives[natives['Gender']=='f']['Weight'])
x_WeightM, y_WeightM = hlp.ecdf(natives[natives['Gender']=='m']['Weight'])

_ = plt.subplot(2,2,3)
_ = plt.plot(x_WeightF, y_WeightF, marker='.',linestyle = 'none',color=colors[0])
_ = plt.plot(x_WeightM, y_WeightM, marker='.',linestyle = 'none',color=colors[1])
_ = plt.margins(0.02)
_ = plt.legend(('Female', 'Male'), loc='lower right')
_ = plt.xlabel('Weight (g)')
_ = plt.ylabel('ECDF')

Plotting Cumulative Distribution Function with 888 samples...

Looking at measures over time

Heatmaps to show concentration of samples per Annuli and Gender (Male)

In [17]:
#2d histogram Carapace / Plastron / Weight - Females
sns.reset_orig()
#plt.style.use('seaborn-notebook')
plt.rcParams['figure.dpi'] = 300
## Lineplot Age + Weight, Carapace, Plastron

### FEMALE
females = natives[natives['Gender']=='f']
females = females[females['new_annuli']!=0]
print ("Plotting Histogram Weight, Carapace and Plastron with " + str(females.Weight.count()) + ' samples...')

_ = plt.figure(3)
_1 =plt.subplot(2,2,1)

#_ = plt.scatter(females['Annuli'], females['Carapace'])
_ = plt.hist2d(females['new_annuli'], females['Carapace'],cmap='Reds',bins=(10,10))
_ = plt.colorbar()
_ = plt.ylabel('Carapace')

_2 =plt.subplot(2,2,2,sharey=_1)
#_ = plt.scatter(females['Annuli'],females['Plastron'])
_ = plt.hist2d(females['new_annuli'], females['Plastron'],cmap='Reds',bins=(10,10))
_ = plt.colorbar()
_ = plt.ylabel('Plastron')

_ = plt.suptitle('Native Females Histogram ')
_ = plt.subplot(2,2,3)
#_ = plt.scatter(females['Annuli'], females['Weight'],s=10)
_ = plt.hist2d(females['new_annuli'], females['Weight'],cmap='Reds',bins=(10,10))
_ = plt.colorbar()
_ = plt.xlabel('Annuli')
_ = plt.ylabel('Weight')
_ = plt.tight_layout(rect=[0, 0.03, 1, 0.95])

Plotting Histogram Weight, Carapace and Plastron with 358 samples...

Heatmaps to show concentration of samples per Annuli and Gender (Female)

In [19]:
##2d histogram Carapace / Plastron / Weight - Males
sns.reset_orig()
#plt.style.use('seaborn-notebook')
plt.rcParams['figure.dpi'] = 300
## Lineplot Age + Weight, Carapace, Plastron

### MALE
males = natives[natives['Gender']=='m']
males = males[males['new_annuli']!=0]
print ("Plotting Histogram Weight, Carapace and Plastron with " + str(males.Weight.count()) + ' samples...')

_ = plt.figure(3)

_1 =plt.subplot(2,2,1)
#_ = plt.scatter(males['Annuli'], males['Carapace'])
_ = plt.hist2d(males['new_annuli'], males['Carapace'],cmap='Blues',bins=(10,10))
_ = plt.colorbar()
_ = plt.ylabel('Carapace')

_2 =plt.subplot(2,2,2,sharey=_1)
#_ = plt.scatter(males['Annuli'],males['Plastron'])
_ = plt.hist2d(males['new_annuli'], males['Plastron'],cmap='Blues',bins=(10,10))
_ = plt.colorbar()
_ = plt.ylabel('Plastron')

_ = plt.suptitle('Native Males Histogram ')
_ = plt.subplot(2,2,3)
#_ = plt.scatter(males['Annuli'], males['Weight'],s=10)
_ = plt.hist2d(males['new_annuli'], males['Weight'],cmap='Blues',bins=(10,10))
_ = plt.colorbar()
_ = plt.xlabel('Annuli')
_ = plt.ylabel('Weight')
_ = plt.tight_layout(rect=[0, 0.03, 1, 0.95])

Plotting Histogram Weight, Carapace and Plastron with 315 samples...

Scatterplots to show concentration of samples per Annuli and Gender (Female)

In [22]:
### Swarmplot with Hue (Keep this?)
natives = natives[natives['new_annuli']!=0]

#colors = [sns.xkcd_rgb['pale red'],sns.xkcd_rgb['denim blue'],sns.xkcd_rgb['medium green']]
colors = {'f':sns.xkcd_rgb['pale red'], 'm':sns.xkcd_rgb['denim blue'], 'unknown':sns.xkcd_rgb['medium green']}
plt.rcParams['figure.dpi'] = 500
print ("Plotting swarm plot with hue with " + str(natives.Weight.count()) + ' samples...')
sns.set()
_ = plt.figure(4)
_ = plt.suptitle('Native Tutles Swarmplots with hue by Gender')
_1 =plt.subplot(2,2,1)
#_ = plt.plot( 'Annuli', 'Carapace', data=natives, linestyle='', marker='o', markersize=0.7)
#_ = plt.scatter(natives['Annuli'],natives['Carapace'],s=10,c=natives['Gender'].apply(lambda x: colors[x]))
_ = sns.swarmplot(x='new_annuli', y='Carapace', hue='Gender',data=natives,dodge=False,size=2,palette=colors)
#_ = sns.lmplot(x='Annuli', y='Carapace', hue='Gender',data=natives,size=2,palette=colors)
#_ = sns.stripplot(x='Annuli', y='Carapace', hue='Gender',data=natives,dodge=True,jitter=True,size=2,palette=colors)
_ = plt.ylabel('Carapace')
_ = plt.xticks(rotation=45)

_2 = plt.subplot(2,2,2)
_ = sns.swarmplot(x='new_annuli', y='Plastron', hue='Gender',data=natives,dodge=False,size=2,palette=colors)
_ = plt.ylabel('Plastron')
_ = plt.xticks(rotation=45)
_ = plt.tight_layout(rect=[0, 0.03, 1, 0.95])

_ = plt.subplot(2,2,3)
_ = sns.swarmplot(x='new_annuli', y='Weight', hue='Gender',data=natives,dodge=False,size=2,palette=colors)
_ = plt.xlabel('Annuli')
_ = plt.xticks(rotation=45)
_ = plt.ylabel('Weight')

Plotting swarm plot with hue with 673 samples...
In [10]:
### Swarmplot with Hue and Linnear regression (Keep this?)
colors = [sns.xkcd_rgb['pale red'],sns.xkcd_rgb['denim blue'],sns.xkcd_rgb['medium green']]
natives = natives[natives['Annuli']!=0]
females = natives[natives['Gender']=='f']
males = natives[natives['Gender']=='m']
captureLocations = natives['Capture Location'].unique()
plt.rcParams['figure.dpi'] = 800

## regression calculation
def get_Linear_Regression_Stats(dataframe,x,y,GenderFilter,LocationFilter,echo=False):
 
    dataframe = dataframe[dataframe['Gender']==GenderFilter]
    dataframe = dataframe[dataframe['Capture Location']==LocationFilter]
    slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(dataframe[x], dataframe[y])
    if echo:
        print( "%s / %s (Filter: %s + %s)" % (x,y,GenderFilter,LocationFilter))
        print('    slope     = %.2f' % slope)
        print('    intercept = %.2f' % intercept)
        print('    r_value   = %.2f' % r_value)
        print('    p_value   = %f' % p_value)
        print('    std_err   = %.2f' % std_err)
    return (slope, intercept, r_value, p_value, std_err)

Scatterplots to show metrics per survey location + linear regression

In [11]:
# scatterplot with linear digression 
#sns.set()
import statsmodels
natives = natives[natives['Gender']!='unknown']
is_robust = False


### Swarmplot with Hue and Linear Digression 
natives = natives[natives['Annuli']!=0]
#colors = [sns.xkcd_rgb['pale red'],sns.xkcd_rgb['denim blue'],sns.xkcd_rgb['medium green']]
colors = {'f':sns.xkcd_rgb['pale red'], 'm':sns.xkcd_rgb['denim blue'], 'unknown':sns.xkcd_rgb['medium green']}
plt.rcParams['figure.dpi'] = 300
print ("Plotting scatter plot with hue and linear digression with " + str(natives.Weight.count()) + ' samples...')
if (is_robust):
    print ("Estimating a robust regression. This will de-weight outliers. Tnis is substantially more computationally intensive")
sns.set()
_ = plt.figure(5)
_ = sns.lmplot(x='Annuli', y='Carapace', hue='Gender',col="Capture Location",data=natives,palette=colors,scatter=True, x_jitter=.5,y_jitter=.1,scatter_kws={"s": 20},robust=is_robust,legend_out=False)
_ = plt.ylabel('Carapace')
_ = sns.lmplot(x='Annuli', y='Plastron', hue='Gender',col="Capture Location",data=natives,palette=colors,scatter=True, x_jitter=.5,y_jitter=.1,scatter_kws={"s": 20},robust=is_robust,legend_out=False)
_ = plt.ylabel('Plastron')
_ = sns.lmplot(x='Annuli', y='Weight', hue='Gender',col="Capture Location",data=natives,palette=colors,scatter=True, x_jitter=.5,y_jitter=.1,scatter_kws={"s": 20},robust=is_robust,legend_out=False)
_ = plt.ylabel('Weight')

Plotting scatter plot with hue and linear digression with 673 samples...

/usr/local/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval

<Figure size 1920x1440 with 0 Axes>
In [30]:
# scatterplot with linear digression 
#sns.set()
import statsmodels
natives = natives[natives['Gender']!='unknown']
is_robust = False


### Swarmplot with Hue and Linear Digression 
natives = natives[natives['new_annuli']!=0]
#colors = [sns.xkcd_rgb['pale red'],sns.xkcd_rgb['denim blue'],sns.xkcd_rgb['medium green']]
colors = {'f':sns.xkcd_rgb['pale red'], 'm':sns.xkcd_rgb['denim blue'], 'unknown':sns.xkcd_rgb['medium green']}
plt.rcParams['figure.dpi'] = 300
print ("NEW ANNULI - Plotting scatter plot with hue and linear digression with " + str(natives.Weight.count()) + ' samples...')
if (is_robust):
    print ("Estimating a robust regression. This will de-weight outliers. Tnis is substantially more computationally intensive")
sns.set()
_ = plt.figure(5)
# _ = sns.lmplot(x='new_annuli', y='Carapace', hue='Gender',col="Capture Location",data=natives,palette=colors,scatter=True, x_jitter=.5,y_jitter=.1,scatter_kws={"s": 20},robust=is_robust,legend_out=False)
# _ = plt.ylabel('Carapace')
# _ = sns.lmplot(x='new_annuli', y='Plastron', hue='Gender',col="Capture Location",data=natives,palette=colors,scatter=True, x_jitter=.5,y_jitter=.1,scatter_kws={"s": 20},robust=is_robust,legend_out=False)
# _ = plt.ylabel('Plastron')
# _ = sns.lmplot(x='new_annuli', y='Weight', hue='Gender',col="Capture Location",data=natives,palette=colors,scatter=True, x_jitter=.5,y_jitter=.1,scatter_kws={"s": 20},robust=is_robust,legend_out=False)
# _ = plt.ylabel('Weight')

# _ = sns.lmplot(x='new_annuli', y='Carapace', hue='Gender',col="Capture Location",data=natives,palette=colors,scatter=True,order=2, ci=None, scatter_kws={"s": 80},legend_out=False)
# _ = plt.ylabel('Carapace')
# _ = sns.lmplot(x='new_annuli', y='Plastron', hue='Gender',col="Capture Location",data=natives,palette=colors,scatter=True,order=2, ci=None, scatter_kws={"s": 80},legend_out=False)
# _ = plt.ylabel('Plastron')
# _ = sns.lmplot(x='new_annuli', y='Weight', hue='Gender',col="Capture Location",data=natives,palette=colors,scatter=True, order=2, ci=None, scatter_kws={"s": 80},legend_out=False)
# _ = plt.ylabel('Weight')

_ = sns.lmplot(x='new_annuli', y='Carapace', hue='Gender',col="Capture Location",data=natives,palette=colors,scatter=True,lowess=True,legend_out=False,x_jitter=.5,y_jitter=.1)
_ = plt.ylabel('Carapace')
_ = sns.lmplot(x='new_annuli', y='Plastron', hue='Gender',col="Capture Location",data=natives,palette=colors,scatter=True,lowess=True,legend_out=False,x_jitter=.5,y_jitter=.1)
_ = plt.ylabel('Plastron')
_ = sns.lmplot(x='new_annuli', y='Weight', hue='Gender',col="Capture Location",data=natives,palette=colors,scatter=True,lowess=True,legend_out=False,x_jitter=.5,y_jitter=.1)
_ = plt.ylabel('Weight')

NEW ANNULI - Plotting scatter plot with hue and linear digression with 673 samples...

<Figure size 1920x1440 with 0 Axes>
In [13]:
# Print Regression Statistics
print('Linnear Regressions...')
for locations in captureLocations:
    for interestingValue in ['Carapace','Plastron','Weight']:
        for gender in ['f','m']:
            get_Linear_Regression_Stats(natives,'Annuli',interestingValue,gender,locations,True)

Linnear Regressions...
Annuli / Carapace (Filter: f + Gresham)
    slope     = 10.02
    intercept = 76.21
    r_value   = 0.88
    p_value   = 0.000000
    std_err   = 0.32
Annuli / Carapace (Filter: m + Gresham)
    slope     = 9.77
    intercept = 64.84
    r_value   = 0.84
    p_value   = 0.000000
    std_err   = 0.39
Annuli / Plastron (Filter: f + Gresham)
    slope     = 9.85
    intercept = 70.54
    r_value   = 0.88
    p_value   = 0.000000
    std_err   = 0.32
Annuli / Plastron (Filter: m + Gresham)
    slope     = 8.94
    intercept = 61.70
    r_value   = 0.83
    p_value   = 0.000000
    std_err   = 0.36
Annuli / Weight (Filter: f + Gresham)
    slope     = 97.48
    intercept = -156.55
    r_value   = 0.88
    p_value   = 0.000000
    std_err   = 3.09
Annuli / Weight (Filter: m + Gresham)
    slope     = 56.01
    intercept = -64.17
    r_value   = 0.83
    p_value   = 0.000000
    std_err   = 2.33
Annuli / Carapace (Filter: f + Mason Flats)
    slope     = 9.95
    intercept = 91.35
    r_value   = 0.87
    p_value   = 0.000000
    std_err   = 0.65
Annuli / Carapace (Filter: m + Mason Flats)
    slope     = 6.33
    intercept = 100.68
    r_value   = 0.84
    p_value   = 0.000000
    std_err   = 0.61
Annuli / Plastron (Filter: f + Mason Flats)
    slope     = 9.17
    intercept = 88.54
    r_value   = 0.85
    p_value   = 0.000000
    std_err   = 0.66
Annuli / Plastron (Filter: m + Mason Flats)
    slope     = 5.81
    intercept = 93.94
    r_value   = 0.82
    p_value   = 0.000000
    std_err   = 0.61
Annuli / Weight (Filter: f + Mason Flats)
    slope     = 95.06
    intercept = -41.57
    r_value   = 0.87
    p_value   = 0.000000
    std_err   = 6.41
Annuli / Weight (Filter: m + Mason Flats)
    slope     = 43.44
    intercept = 92.62
    r_value   = 0.82
    p_value   = 0.000000
    std_err   = 4.55
In [14]:
#Hyposis: same distribution in all capture locations
native_portland = natives[(natives['Capture Location']=='Mason Flats')]
native_gresham = natives[(natives['Capture Location']=='Gresham') ]
#&(natives['Gender']=='f')
genders = ['f','m']
dimension = 'Weight'
_ = plt.figure(6)
i = 0
for gender in genders:
    i += 1
    _ = plt.subplot(1,2,i)
    for _ in range(100):
        # Generate permutation samples
        native_portland_gender = native_portland[native_portland['Gender']==gender]
        native_gresham_gender = native_portland[native_portland['Gender']==gender]
        perm_sample_1, perm_sample_2 = hlp.permutation_sample(native_portland_gender[dimension],native_gresham_gender[dimension])

        # Compute ECDFs
        x_1, y_1 = hlp.ecdf(perm_sample_1)
        x_2, y_2 = hlp.ecdf(perm_sample_2)

        # Plot ECDFs of permutation sample
        _ = plt.plot(x_1, y_1, marker='.', linestyle='none',
                     color='green', alpha=0.02)
        _ = plt.plot(x_2, y_2, marker='.', linestyle='none',
                     color='orange', alpha=0.02)

    # Create and plot ECDFs from original data
    x_1, y_1 = hlp.ecdf(native_portland_gender[dimension])
    x_2, y_2 = hlp.ecdf(native_portland_gender[dimension])
    _ = plt.plot(x_1, y_1, marker='.', linestyle='none', color='green',label='Portland')
    _ = plt.plot(x_2, y_2, marker='.', linestyle='none', color='orange',label='Gresham')
    _ = plt.legend()
    _ = plt.title(gender)
    # Label axes, set margin, and show plot
    plt.margins(0.02)
    _ = plt.xlabel(dimension)
    _ = plt.ylabel('ECDF')
plt.show()
In [15]:
#Hyposis: DIFFERENT distribution between genders
native_females = natives[(natives['Gender']=='f')]
native_males = natives[(natives['Gender']=='m')]
#&(natives['Gender']=='f')
dimension = 'Weight'
_ = plt.figure(7)
for _ in range(100):
    # Generate permutation samples
    perm_sample_1, perm_sample_2 = hlp.permutation_sample(native_females[dimension],native_males[dimension])

    # Compute ECDFs
    x_1, y_1 = hlp.ecdf(perm_sample_1)
    x_2, y_2 = hlp.ecdf(perm_sample_2)

    # Plot ECDFs of permutation sample
    _ = plt.plot(x_1, y_1, marker='.', linestyle='none',
                 color='red', alpha=0.02)
    _ = plt.plot(x_2, y_2, marker='.', linestyle='none',
                 color='blue', alpha=0.02)

# Create and plot ECDFs from original data
x_1, y_1 = hlp.ecdf(native_females[dimension])
x_2, y_2 = hlp.ecdf(native_males[dimension])
_ = plt.plot(x_1, y_1, marker='.', linestyle='none', color='red',label='female')
_ = plt.plot(x_2, y_2, marker='.', linestyle='none', color='blue',label='male')
_ = plt.legend()
_ = plt.title('Permutation Sampling by Gender')
# Label axes, set margin, and show plot
plt.margins(0.02)
_ = plt.xlabel(dimension)
_ = plt.ylabel('ECDF')
plt.show()

Correlation

In [16]:
native_females.info()
corrleations = native_females.loc[slice(None),['Annuli','Weight','Carapace','Plastron']].corr()

_ = sns.heatmap(corrleations,annot=True)
_ = plt.xticks(rotation=45)
_ = plt.show()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 358 entries, 0 to 203
Data columns (total 38 columns):
Date                            358 non-null datetime64[ns]
ID                              358 non-null object
Time                            237 non-null object
Surveyor(s)                     75 non-null object
Weather                         165 non-null object
Capture Location                358 non-null object
Capture Method                  357 non-null object
Recapture                       357 non-null object
Species                         358 non-null object
Gender                          358 non-null object
Annuli                          358 non-null int8
Weight                          358 non-null float32
Carapace                        358 non-null float32
Plastron                        358 non-null float32
Gravid                          296 non-null object
Nest#                           13 non-null object
Nest Site                       16 non-null object
Marginal Scutes/side            352 non-null object
Carapace Scute Abnormalities    326 non-null object
Plastron Scute Abnormalities    297 non-null object
Other Abnormalities             226 non-null object
Photos                          273 non-null object
Comments                        76 non-null object
Unnamed: 23                     0 non-null float64
Unnamed: 24                     1 non-null object
Source                          358 non-null object
Trap #                          239 non-null object
Comments.1                      7 non-null object
Unnamed: 25                     0 non-null float64
Unnamed: 26                     1 non-null object
Age_To_Weight                   358 non-null float32
Annuli_Group                    357 non-null object
recapture_count                 358 non-null int64
lowest_annuli                   358 non-null float64
first_date                      358 non-null datetime64[ns]
date_year                       358 non-null int64
first_date_year                 358 non-null float64
new_annuli                      358 non-null float64
dtypes: datetime64[ns](2), float32(4), float64(5), int64(2), int8(1), object(24)
memory usage: 101.0+ KB